Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "109" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 30 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 28 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2459845 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | 100.00% | 0.00% | 0.614112 | 19.452638 | 0.729516 | 40.264456 | -0.204533 | 16.551647 | 1.185537 | 0.510105 | 0.7639 | 0.0457 | 0.5186 | 0.000000 | 0.000000 |
| 2459844 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 0.330661 | 15.460425 | -0.880105 | 5.020512 | 0.384747 | 1.866733 | 0.675409 | 5.007849 | 0.0277 | 0.0239 | 0.0017 | nan | nan |
| 2459842 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | 100.00% | 0.00% | 0.275992 | 13.011055 | -0.119217 | 9.743244 | 0.145801 | -0.674238 | -0.041888 | 0.266163 | 0.7684 | 0.0354 | 0.4655 | 5.716460 | 1.282109 |
| 2459841 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 1.063289 | 16.167669 | -0.458133 | 3.375338 | 0.178580 | 2.805329 | 1.317490 | 1.514526 | 0.0272 | 0.0237 | 0.0019 | nan | nan |
| 2459840 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | - | - | -0.900478 | 0.134596 | -0.046888 | -1.659686 | 1.022786 | -0.614660 | 0.488936 | -1.022406 | 0.0265 | 0.0236 | 0.0018 | nan | nan |
| 2459839 | digital_ok | 0.00% | - | - | - | - | - | 0.559337 | 1.373236 | -0.290400 | 0.901489 | -0.218249 | -1.149014 | 0.422110 | -1.864521 | nan | nan | nan | nan | nan |
| 2459838 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 332.707608 | 332.895689 | inf | inf | 12096.394933 | 11936.834493 | 8056.773851 | 7883.789311 | nan | nan | nan | 0.000000 | 0.000000 |
| 2459836 | digital_ok | - | 100.00% | 100.00% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.0380 | 0.0484 | 0.0027 | nan | nan |
| 2459835 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | - | - | 0.840910 | -0.611411 | -0.031276 | -1.127335 | -0.005665 | -0.279339 | -0.766062 | -0.891921 | 0.0348 | 0.0354 | 0.0010 | nan | nan |
| 2459833 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | - | - | 0.796426 | -0.399366 | -0.563130 | -0.516481 | 0.362877 | 0.554625 | -0.028196 | -0.667642 | 0.0329 | 0.0339 | 0.0009 | nan | nan |
| 2459832 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.097921 | 0.409664 | 0.042781 | 0.399700 | 1.336038 | -0.686722 | 2.682357 | 0.549105 | 0.7473 | 0.4707 | 0.5553 | 1.569230 | 1.515758 |
| 2459831 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | - | - | 0.414179 | 1.221504 | -0.217172 | 1.088131 | -0.522044 | -1.498029 | 0.229417 | -1.282522 | 0.0515 | 0.0368 | 0.0024 | nan | nan |
| 2459830 | digital_ok | 0.00% | 0.00% | 2.69% | 0.00% | 2.63% | 0.00% | -1.044249 | 0.186808 | 0.144322 | 0.762188 | 0.631555 | 1.146296 | -0.269508 | -0.397857 | 0.7451 | 0.4584 | 0.5557 | 1.693811 | 1.576328 |
| 2459829 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.65% | 99.35% | 0.980410 | -0.070953 | -0.312226 | 0.708690 | -0.937996 | -1.095363 | -0.468835 | -0.605490 | 0.6845 | 0.5916 | 0.4207 | 14.892859 | 14.887592 |
| 2459828 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | -0.158162 | 0.516605 | -0.279096 | 0.789499 | -0.647754 | 0.606927 | -0.663599 | 0.193819 | 0.7419 | 0.4817 | 0.5336 | 1.941254 | 1.806998 |
| 2459827 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | -0.558301 | 0.118593 | 0.396370 | 0.832868 | -0.299683 | -0.558419 | 0.274565 | 1.240673 | 0.6968 | 0.6051 | 0.4178 | 1.567591 | 1.422756 |
| 2459826 | digital_ok | 0.00% | 16.13% | 16.13% | 0.00% | 15.79% | 0.00% | -0.835091 | -0.007480 | 0.740172 | 0.612190 | -1.033502 | 0.393463 | -1.020567 | -0.290278 | 0.6628 | 0.4535 | 0.4501 | 1.549987 | 1.250815 |
| 2459825 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | -0.878918 | 0.340045 | -0.045965 | 0.794073 | -0.311930 | -0.356912 | 0.258265 | -0.028627 | 0.0851 | 0.0780 | 0.0159 | 0.000000 | 0.000000 |
| 2459824 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | -0.735342 | 0.103254 | -0.215338 | 1.084964 | -0.732753 | -0.452963 | -0.286730 | -0.539648 | 0.0880 | 0.0818 | 0.0193 | 0.000000 | 0.000000 |
| 2459823 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | -0.990560 | 0.565278 | 0.793331 | 0.752447 | -0.700674 | 1.854382 | -0.495024 | 1.274363 | 0.0847 | 0.0794 | 0.0208 | 0.000000 | 0.000000 |
| 2459822 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | -0.415237 | 1.154458 | 0.333437 | 0.803908 | -0.515048 | -0.051364 | 4.132036 | 0.501268 | 0.0826 | 0.0877 | 0.0160 | 0.000000 | 0.000000 |
| 2459821 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | -1.395479 | 0.207507 | 0.156551 | 0.522784 | -0.329551 | 0.467607 | 1.594097 | -0.429207 | 0.0783 | 0.0757 | 0.0175 | 0.928684 | 0.940439 |
| 2459820 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | -0.749923 | 0.588695 | 0.180820 | 0.812174 | -0.417933 | -0.819227 | 1.456128 | 0.195629 | 0.0779 | 0.0769 | 0.0147 | 0.000000 | 0.000000 |
| 2459817 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | -0.763024 | 0.959486 | -0.103191 | 0.355583 | -0.678477 | 0.361199 | 0.490988 | 0.439564 | 0.0767 | 0.0781 | 0.0137 | 0.000000 | 0.000000 |
| 2459816 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | -0.898271 | 0.445945 | 0.394811 | 1.150407 | -0.867756 | 1.134557 | -0.792197 | -0.006513 | 0.0659 | 0.0793 | 0.0166 | 1.429166 | 1.370774 |
| 2459815 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | -0.721976 | 0.417380 | 0.020255 | 0.983664 | -1.093266 | 1.162364 | -0.804363 | 0.011804 | 0.0819 | 0.0792 | 0.0149 | 1.407782 | 1.405488 |
| 2459814 | digital_ok | 0.00% | - | - | - | - | - | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2459813 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 0.506203 | 0.307774 | -0.226576 | -0.150362 | -1.254363 | -0.543180 | 0.850318 | 0.185144 | 0.1187 | 0.1091 | 0.0275 | 0.000000 | 0.000000 |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 109 | N10 | digital_ok | nn Power | 40.264456 | 19.452638 | 0.614112 | 40.264456 | 0.729516 | 16.551647 | -0.204533 | 0.510105 | 1.185537 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 109 | N10 | digital_ok | nn Shape | 15.460425 | 0.330661 | 15.460425 | -0.880105 | 5.020512 | 0.384747 | 1.866733 | 0.675409 | 5.007849 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 109 | N10 | digital_ok | nn Shape | 13.011055 | 0.275992 | 13.011055 | -0.119217 | 9.743244 | 0.145801 | -0.674238 | -0.041888 | 0.266163 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 109 | N10 | digital_ok | nn Shape | 16.167669 | 1.063289 | 16.167669 | -0.458133 | 3.375338 | 0.178580 | 2.805329 | 1.317490 | 1.514526 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 109 | N10 | digital_ok | ee Temporal Variability | 1.022786 | -0.900478 | 0.134596 | -0.046888 | -1.659686 | 1.022786 | -0.614660 | 0.488936 | -1.022406 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 109 | N10 | digital_ok | nn Shape | 1.373236 | 1.373236 | 0.559337 | 0.901489 | -0.290400 | -1.149014 | -0.218249 | -1.864521 | 0.422110 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 109 | N10 | digital_ok | nn Power | inf | 332.895689 | 332.707608 | inf | inf | 11936.834493 | 12096.394933 | 7883.789311 | 8056.773851 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 109 | N10 | digital_ok | ee Shape | 0.840910 | -0.611411 | 0.840910 | -1.127335 | -0.031276 | -0.279339 | -0.005665 | -0.891921 | -0.766062 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 109 | N10 | digital_ok | ee Shape | 0.796426 | -0.399366 | 0.796426 | -0.516481 | -0.563130 | 0.554625 | 0.362877 | -0.667642 | -0.028196 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 109 | N10 | digital_ok | ee Temporal Discontinuties | 2.682357 | 0.097921 | 0.409664 | 0.042781 | 0.399700 | 1.336038 | -0.686722 | 2.682357 | 0.549105 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 109 | N10 | digital_ok | nn Shape | 1.221504 | 0.414179 | 1.221504 | -0.217172 | 1.088131 | -0.522044 | -1.498029 | 0.229417 | -1.282522 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 109 | N10 | digital_ok | nn Temporal Variability | 1.146296 | -1.044249 | 0.186808 | 0.144322 | 0.762188 | 0.631555 | 1.146296 | -0.269508 | -0.397857 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 109 | N10 | digital_ok | ee Shape | 0.980410 | -0.070953 | 0.980410 | 0.708690 | -0.312226 | -1.095363 | -0.937996 | -0.605490 | -0.468835 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 109 | N10 | digital_ok | nn Power | 0.789499 | 0.516605 | -0.158162 | 0.789499 | -0.279096 | 0.606927 | -0.647754 | 0.193819 | -0.663599 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 109 | N10 | digital_ok | nn Temporal Discontinuties | 1.240673 | -0.558301 | 0.118593 | 0.396370 | 0.832868 | -0.299683 | -0.558419 | 0.274565 | 1.240673 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 109 | N10 | digital_ok | ee Power | 0.740172 | -0.007480 | -0.835091 | 0.612190 | 0.740172 | 0.393463 | -1.033502 | -0.290278 | -1.020567 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 109 | N10 | digital_ok | nn Power | 0.794073 | 0.340045 | -0.878918 | 0.794073 | -0.045965 | -0.356912 | -0.311930 | -0.028627 | 0.258265 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 109 | N10 | digital_ok | nn Power | 1.084964 | -0.735342 | 0.103254 | -0.215338 | 1.084964 | -0.732753 | -0.452963 | -0.286730 | -0.539648 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 109 | N10 | digital_ok | nn Temporal Variability | 1.854382 | 0.565278 | -0.990560 | 0.752447 | 0.793331 | 1.854382 | -0.700674 | 1.274363 | -0.495024 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 109 | N10 | digital_ok | ee Temporal Discontinuties | 4.132036 | -0.415237 | 1.154458 | 0.333437 | 0.803908 | -0.515048 | -0.051364 | 4.132036 | 0.501268 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 109 | N10 | digital_ok | ee Temporal Discontinuties | 1.594097 | 0.207507 | -1.395479 | 0.522784 | 0.156551 | 0.467607 | -0.329551 | -0.429207 | 1.594097 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 109 | N10 | digital_ok | ee Temporal Discontinuties | 1.456128 | -0.749923 | 0.588695 | 0.180820 | 0.812174 | -0.417933 | -0.819227 | 1.456128 | 0.195629 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 109 | N10 | digital_ok | nn Shape | 0.959486 | -0.763024 | 0.959486 | -0.103191 | 0.355583 | -0.678477 | 0.361199 | 0.490988 | 0.439564 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 109 | N10 | digital_ok | nn Power | 1.150407 | 0.445945 | -0.898271 | 1.150407 | 0.394811 | 1.134557 | -0.867756 | -0.006513 | -0.792197 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 109 | N10 | digital_ok | nn Temporal Variability | 1.162364 | 0.417380 | -0.721976 | 0.983664 | 0.020255 | 1.162364 | -1.093266 | 0.011804 | -0.804363 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 109 | N10 | digital_ok | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 109 | N10 | digital_ok | ee Temporal Discontinuties | 0.850318 | 0.307774 | 0.506203 | -0.150362 | -0.226576 | -0.543180 | -1.254363 | 0.185144 | 0.850318 |